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Self-Supervised Learning of Speech Representation via Redundancy Reduction
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2023
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Gesellschaft für Informatik e.V.
Zusammenfassung
Our proposed research aims to contribute to the field of SSL for speech processing by developing representations that effectively capture latent speaker statistics. A comprehensive evaluation in various downstream tasks will provide a thorough assessment of the representations’ suitability and performance. The outcomes of this research will advance our understanding and utilization of SSL in speech representation learning, ultimately enhancing speaker-related applications and their practical implications.